The motivation for federated credit risk models Federated learning is a machine learning technique that is receiving increased attention in diverse data driven application domains that have data privacy concerns. The essence of the concept is to train algorithms across decentralized servers, each holding their own local data samples, hence without the need to exchange potentially sensitive information. The construction of a common model is achieved through the exchange of derived data (gradients, parameters, weights etc).
Overview of the Julia-Python-R Universe A new Open Risk Manual entry offers a side-by-side review of the main open source ecosystems supporting the Data Science domain: Julia, Python, R, sometimes abbreviated as Jupyter. Motivation A large component of Quantitative Risk Management relies on data processing and quantitative tools (aka Data Science). In recent years open source software targeting Data Science finds increased adoption in diverse applications. The Overview of the Julia-Python-R Universe article is a side by side comparison of a wide range of aspects of Python, Julia and R language ecosystems.
The working definition of a Data Scientist seems to be in the current overheated environment: doing whatever it takes to get the job done in a digital #tech domain that we have long neglected but which is now coming back to haunt us! That is nice urgency while it lasts, but it is not a serious job description for the future. You will always find entrepreneurial institutions to offer degrees and certifications on the latest trending hashtag.
Are you getting a bit tired with all the machine learning ballyhoo? You can blame it all on a German mathematician(*), Carl Friedrich Gauss, who started the futuristic “mega-trend” back in 1809: He showed us how to “train” a straight line to pass nicely through a cloud of unruly, scattered data points. To find, in effect, a path of least embarrassment. Two+ centuries later it is still a profitable enterprise to invent elaborate variations of that theme, now going under the more exalted name of “supervised learning“, which may or may not include “deep learning”.
Most developers know (or get to know quickly once they join a team) that programming languages are as much about communicating with other developers as they are about instructing the computer. Which raises the interesting question: If programming languages were human languages which one would be which? Here is a (tonque-in-cheek mind you!) compilation of a mapping between programming languages and human languages. Suggestions / corrections are welcome via the feedback button
Job Specification for an Artificially Intelligent Banker The Artificially Intelligent Banker is responsible for the overall management of the AI2H (AI to Human) lending department. The following requirements (job specifications) were determined by extensive data mining analysis and derived from the job description as crucial for success in the Artificially Intelligent Banker role. The successful candidate for the Artificially Intelligent Banker position will possess the following qualifications: Experience Evidence of 8-12 months of continuous uptime without rebooting Progressively more responsible positions in human interface roles, preferably in a similar industry in two different decentralized autonomous firms Indicatively human sales interaction experience with least 10 mln Human subjects is required.